Modelling the adsorption of natural organic matter on Ag (111) surface: Insights from dispersion corrected density functional theory calculations.
J Mol Graph Model
; 92: 313-319, 2019 11.
Article
in En
| MEDLINE
| ID: mdl-31442937
ABSTRACT
Understanding the nature of the interactions between natural organic matter (NOM) and engineered nanoparticles (ENPs) is of crucial importance in understanding the fate and behaviour of engineered nanoparticles in the environment. In the present study, dispersion-corrected density functional theory (DFT-D) has been used to elucidate the molecule-surface interactions of higher molecular weight (HMW) NOM ambiguously present in the aquatic systems, namely humic acid (HA), fulvic acid (FA) and protein Cryptochrome (Cry) on Ag (111) surface. Investigations were done in the gas phase and to mimic real biological environment, water has been used as a solvent within the conductor-like screening model (COSMO) framework. The calculated adsorption energies for HA, FA and Cry on Ag (111) surface were -27.90 (-18.45) kcal/mol, -38.28 (-18.68) kcal/mol and -143.89 (-150.82) kcal/mol respectively in the gas (solvent) phase and the equilibrium distances between the surface and HA, FA and Cry molecules were 1.87 (2.18) Å, 2.31(2.31) Å and 1.91 (1.70) Å respectively in the gas (solvent) phase. In both gas and water phase Cry showed stronger adsorption which means it has a stronger interaction with Ag (111) surface compared to HA and FA. The results for adsorption energy, solvation energy, isosurface of charge deformation difference, total density of state and partial density of states indicated that indeed these chosen adsorbates do interact with the surface and are favourable on Ag (111) surface. In terms of charge transfer, one of many calculated descriptors in this study, electrophilicity (ω) concur that charge transfer will take place from the adsorbates to Ag (111) surface.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Silver
/
Density Functional Theory
/
Models, Theoretical
Language:
En
Journal:
J Mol Graph Model
Journal subject:
BIOLOGIA MOLECULAR
Year:
2019
Document type:
Article